OpenRaiser/Intern-Atlas huggingface dataset:
Missing Value Imputation With Unsupervised Backpropagation
Venue: arXiv cs.NE 2013
Year: 2013

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 datasets and 9 supervised learning algorithms that classification accuracy is usually higher when randomly-withheld values are imputed using UBP, rather than with other methods.
Polarizing Double Negation Translations
Venue: arXiv cs.LO 2013
Year: 2013

Double-negation translations are used to encode and decode classical proofs in intuitionistic logic. We show that, in the cut-free fragment, we can simplify the translations and introduce fewer negations. To achieve this, we consider the polarization of the formulæ{} and adapt those translation to the different connectives and quantifiers. We show that the embedding results still hold, using a customized version of the focused classical sequent calculus. We also prove the latter equivalent to more usual versions of the sequent calculus. This polarization process allows lighter embeddings, and sheds some light on the relationship between intuitionistic and classical connectives.
On the Proxy Identity Crisis
Venue: arXiv cs.PL 2013
Year: 2013

A proxy, in general, is an object mediating access to an arbitrary target object. The proxy is then intended to be used in place of the target object. Ideally, a proxy is not distinguishable from other objects. Running a program with a proxy leads to the same outcome as running the program with the target object. Even though the approach provides a lot of power to the user, proxies come with a limitation. Because a proxy, wrapping a target object, is a new object and different from its target, the interposition changes the behaviour of some core components. For distinct proxies the double == and triple === equal operator returns false, even if the target object is the same. More precisely, the expected result depends on use case. To overcome this limitation we will discuss alternatives.
The availability of research data declines rapidly with article age
Venue: arXiv cs.DL 2013
Year: 2013

Policies ensuring that research data are available on public archives are increasingly being implemented at the government [1], funding agency [2-4], and journal [5,6] level. These policies are predicated on the idea that authors are poor stewards of their data, particularly over the long term [7], and indeed many studies have found that authors are often unable or unwilling to share their data [8-11]. However, there are no systematic estimates of how the availability of research data changes with time since publication. We therefore requested datasets from a relatively homogenous set of 516 articles published between 2 and 22 years ago, and found that availability of the data was strongly affected by article age. For papers where the authors gave the status of their data, the odds of a dataset being extant fell by 17% per year. In addition, the odds that we could find a working email address for the first, last or corresponding author fell by 7% per year. Our results reinforce the notion that, in the long term, research data cannot be reliably preserved by individual researchers, and further demonstrate the urgent need for policies mandating data sharing via public archives.
Multi-component model of learning and its use for research didactic system
Venue: arXiv cs.CY 2013
Year: 2013

In computer simulation of the learning process is usually assumed that all elements of the training material are assimilated equally durable. But in practice, the knowledge, which a student uses in its operations, are remembered much better. For a more precise study of didactic systems the multi component model of learning are proposed. It takes into account: 1) the transition of weak knowledge in trustworthy knowledge; 2) the difference in the rate of forgetting the trustworthy and weak knowledge. It is assumed that the rate of increase of student's knowledge is proportional to: 1) the difference between the level of the requirements of teachers and the number of learned knowledge; 2) the amount of learned knowledge, raised to some power. Examples of the use of a multi component model for the study of situations in the learning process are considered, the resulting graphs of the student's level of knowledge of the time are presented. A generalized model of learning, which allows to take into account the complexity of the various elements of the educational material are proposed. The possibility of creating a training program for the training of students of pedagogical institutes are considered.
Unsupervised Feature Learning by Deep Sparse Coding
Venue: arXiv cs.LG 2013
Year: 2013

In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks. The main innovation of the framework is that it connects the sparse-encoders from different layers by a sparse-to-dense module. The sparse-to-dense module is a composition of a local spatial pooling step and a low-dimensional embedding process, which takes advantage of the spatial smoothness information in the image. As a result, the new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. Combining the feature representations from multiple layers, DeepSC achieves the state-of-the-art performance on multiple object recognition tasks.
Hybrid Automated Reasoning Tools: from Black-box to Clear-box Integration
Venue: arXiv cs.AI 2013
Year: 2013

Recently, researchers in answer set programming and constraint programming spent significant efforts in the development of hybrid languages and solving algorithms combining the strengths of these traditionally separate fields. These efforts resulted in a new research area: constraint answer set programming (CASP). CASP languages and systems proved to be largely successful at providing efficient solutions to problems involving hybrid reasoning tasks, such as scheduling problems with elements of planning. Yet, the development of CASP systems is difficult, requiring non-trivial expertise in multiple areas. This suggests a need for a study identifying general development principles of hybrid systems. Once these principles and their implications are well understood, the development of hybrid languages and systems may become a well-established and well-understood routine process. As a step in this direction, in this paper we conduct a case study aimed at evaluating various integration schemas of CASP methods.
Generating Shortest Synchronizing Sequences using Answer Set Programming
Venue: arXiv cs.AI 2013
Year: 2013

For a finite state automaton, a synchronizing sequence is an input sequence that takes all the states to the same state. Checking the existence of a synchronizing sequence and finding a synchronizing sequence, if one exists, can be performed in polynomial time. However, the problem of finding a shortest synchronizing sequence is known to be NP-hard. In this work, the usefulness of Answer Set Programming to solve this optimization problem is investigated, in comparison with brute-force algorithms and SAT-based approaches. Keywords: finite automata, shortest synchronizing sequence, ASP
Output Synchronization of Nonlinear Systems under Input Disturbances
Venue: arXiv cs.SY 2013
Year: 2013

We study synchronization of nonlinear systems that satisfy an incremental passivity property. We consider the case where the control input is subject to a class of disturbances, including constant and sinusoidal disturbances with unknown phases and magnitudes and known frequencies. We design a distributed control law that recovers the synchronization of the nonlinear systems in the presence of the disturbances. Simulation results of Goodwin oscillators illustrate the effectiveness of the control law. Finally, we highlight the connection of the proposed control law to the dynamic average consensus estimator developed in [1].
State-of-the Art Motion Estimation in the Context of 3D TV
Venue: arXiv cs.MM 2013
Year: 2013

Progress in image sensors and computation power has fueled studies to improve acquisition, processing, and analysis of 3D streams along with 3D scenes/objects reconstruction. The role of motion compensation/motion estimation (MCME) in 3D TV from end-to-end user is investigated in this chapter. Motion vectors (MVs) are closely related to the concept of disparities, and they can help improving dynamic scene acquisition, content creation, 2D to 3D conversion, compression coding, decompression/decoding, scene rendering, error concealment, virtual/augmented reality handling, intelligent content retrieval, and displaying. Although there are different 3D shape extraction methods, this chapter focuses mostly on shape-from-motion (SfM) techniques due to their relevance to 3D TV. SfM extraction can restore 3D shape information from a single camera data.
Top Down Approach to Multiple Plane Detection
Venue: arXiv cs.CV 2013
Year: 2013

Detecting multiple planes in images is a challenging problem, but one with many applications. Recent work such as J-Linkage and Ordered Residual Kernels have focussed on developing a domain independent approach to detect multiple structures. These multiple structure detection methods are then used for estimating multiple homographies given feature matches between two images. Features participating in the multiple homographies detected, provide us the multiple scene planes. We show that these methods provide locally optimal results and fail to merge detected planar patches to the true scene planes. These methods use only residues obtained on applying homography of one plane to another as cue for merging. In this paper, we develop additional cues such as local consistency of planes, local normals, texture etc. to perform better classification and merging . We formulate the classification as an MRF problem and use TRWS message passing algorithm to solve non metric energy terms and complex sparse graph structure. We show results on challenging dataset common in robotics navigation scenarios where our method shows accuracy of more than 85 percent on average while being close or same as the actual number of scene planes.
The expert game -- Cooperation in social communication
Venue: arXiv cs.SI 2013
Year: 2013

Large parts of professional human communication proceed in a request-reply fashion, whereby requests contain specifics of the information desired while replies can deliver the required information. However, time limitations often force individuals to prioritize some while neglecting others. This dilemma will inevitably force individuals into defecting against some communication partners to give attention to others. Furthermore, communication entirely breaks down when individuals act purely egoistically as replies would never be issued and quest for desired information would always be prioritized. Here we present an experiment, termed "The expert game", where a number of individuals communicate with one-another through an electronic messaging system. By imposing a strict limit on the number of sent messages, individuals were required to decide between requesting information that is beneficial for themselves or helping others by replying to their requests. In the experiment, individuals were assigned the task to find the expert on a specific topic and receive a reply from that expert. Tasks and expertise of each player were periodically re-assigned to randomize the required interactions. Resisting this randomization, a non-random network of cooperative communication between individuals formed. We use a simple Bayesian inference algorithm to model each player's trust in the cooperativity of others with good experimental agreement. Our results suggest that human communication in groups of individuals is strategic and favors cooperation with trusted parties at the cost of defection against others. To establish and maintain trusted links a significant fraction of time-resources is allocated, even in situations where the information transmitted is negligible.
Multi-dimensional Conversation Analysis across Online Social Networks
Venue: arXiv cs.SI 2013
Year: 2013

With the advance of the Internet, ordinary users have created multiple personal accounts on online social networks, and interactions among these social network users have recently been tagged with location information. In this work, we observe user interactions across two popular online social networks, Facebook and Twitter, and analyze which factors lead to retweet/like interactions for tweets/posts. In addition to the named entities, lexical errors and expressed sentiments in these data items, we also consider the impact of shared user locations on user interactions. In particular, we show that geolocations of users can greatly affect which social network post/tweet will be liked/ retweeted. We believe that the results of our analysis can help researchers to understand which social network content will have better visibility.
IVSS Integration of Color Feature Extraction Techniques for Intelligent Video Search Systems
Venue: arXiv cs.CV 2013
Year: 2013

As large amount of visual Information is available on web in form of images, graphics, animations and videos, so it is important in internet era to have an effective video search system. As there are number of video search engine (blinkx, Videosurf, Google, YouTube, etc.) which search for relevant videos based on user keyword or term, But very less commercial video search engine are available which search videos based on visual image/clip/video. In this paper we are recommending a system that will search for relevant video using color feature of video in response of user Query.
3D Interest Point Detection via Discriminative Learning
Venue: arXiv cs.CV 2013
Year: 2013

The task of detecting the interest points in 3D meshes has typically been handled by geometric methods. These methods, while greatly describing human preference, can be ill-equipped for handling the variety and subjectivity in human responses. Different tasks have different requirements for interest point detection; some tasks may necessitate high precision while other tasks may require high recall. Sometimes points with high curvature may be desirable, while in other cases high curvature may be an indication of noise. Geometric methods lack the required flexibility to adapt to such changes. As a consequence, interest point detection seems to be well suited for machine learning methods that can be trained to match the criteria applied on the annotated training data. In this paper, we formulate interest point detection as a supervised binary classification problem using a random forest as our classifier. Among other challenges, we are faced with an imbalanced learning problem due to the substantial difference in the priors between interest and non-interest points. We address this by re-sampling the training set. We validate the accuracy of our method and compare our results to those of five state of the art methods on a new, standard benchmark.
An Energy-Efficient Localization Strategy for Smartphones
Venue: arXiv cs.NI 2013
Year: 2013

In recent years, smartphones have become prevalent. Much attention is being paid to developing and making use of mobile applications that require position information. The Global Positioning System (GPS) is a very popular localization technique used by these applications because of its high accuracy. However, GPS incurs an unacceptable energy consumption, which severely limits the use of smartphones and reduces the battery lifetime. Then an urgent requirement for these applications is a localization strategy that not only provides enough accurate position information to meet users' need but also consumes less energy. In this paper, we present an energy-efficient localization strategy for smartphone applications. On one hand, it can dynamically estimate the next localization time point to avoid unnecessary localization operations. On the other hand, it can also automatically select the energy-optimal localization method. We evaluate the strategy through a series of simulations. Experimental results show that it can significantly reduce the localization energy consumption of smartphones while ensuring a good satisfaction degree.
Hardware and logic implementation of multiple alarm system for GSM BTS rooms
Venue: arXiv cs.SY 2013
Year: 2013

Cellular communication becomes the major mode of communication in present century. With the development of this phase of communication the globalization process is also in its peak of speed. The development of cellular communication is largely depending on the improvement and stability of Base Transceiver Station (BTS) room. So for the purpose of the development of cellular communication a large numbered BTS rooms are installed throughout the world. To ensure proper support from BTS rooms there must be a security system to avoid any unnecessary vulnerability. Therefore multiple alarm system is designed to secure the BTS rooms from any undesired circumstances. This system is designed with a PIC Microcontroller as a main controller and a several sensors are interfaced with it to provide high temperature alarm, smoke alarm, door alarm and water alarm. All these alarms are interfaced with the alarm box in the BTS room which provides the current status directly to Network Management Centre (NMC) of a Global System for Mobile (GSM) communication network.
Detection of Syntactic Aspect Interaction in UML State Diagrams Using Critical Pair Analysis in Graph Transformation
Venue: arXiv cs.SE 2013
Year: 2013

Aspect Oriented Modeling separates crosscutting concerns by defining Aspects and composition mechanisms at the model level. Composition of multiple Aspects will most likely result in more than one Aspect matching the same join points. Consequently, Aspects do not always interact in a predictable manner when woven together. Intended interaction among aspects is designed by the system designer. Unintended interaction (or interference) must be automatically managed. When the woven aspect demonstrates a behavior that is different than its autonomous behavior, then this is a potential interference. Interference has been recently reported in Aspect Oriented Software Development (AOSD) by the industry. Leaving this problem unsolved may result in erratic software behavior and will hinder the adaptation of AOSD by the industry. This identified problem is similar to a phenomenon that exists in graph transformation systems where multiple Graph Transformation rules share some conflicting elements, it is referred to as Critical Pair Analysis and it provides an algebraic-based mechanism to detect and analyze the interaction of the rules. In this paper we propose a framework to detect unintended Aspect interaction at the model level. The proposed framework transforms Aspects modeled in UML State Diagram to Graph Transformation Rules, and then it applies Critical Pair Analysis to detect unintended interactions among aspects. This will enable developers to specify only the order of precedence for intended interaction among aspects without the need to manually investigate unintended interactions for the combinations of every Aspect to every other Aspect in the system. The proposed interaction detection solution is automated, modular, and independent of the base model; which adds the advantage of not having to re-evaluate the interaction each time the base model changes.
Subjectivity Classification using Machine Learning Techniques for Mining Feature-Opinion Pairs from Web Opinion Sources
Venue: arXiv cs.IR 2013
Year: 2013

Due to flourish of the Web 2.0, web opinion sources are rapidly emerging containing precious information useful for both customers and manufactures. Recently, feature based opinion mining techniques are gaining momentum in which customer reviews are processed automatically for mining product features and user opinions expressed over them. However, customer reviews may contain both opinionated and factual sentences. Distillations of factual contents improve mining performance by preventing noisy and irrelevant extraction. In this paper, combination of both supervised machine learning and rule-based approaches are proposed for mining feasible feature-opinion pairs from subjective review sentences. In the first phase of the proposed approach, a supervised machine learning technique is applied for classifying subjective and objective sentences from customer reviews. In the next phase, a rule based method is implemented which applies linguistic and semantic analysis of texts to mine feasible feature-opinion pairs from subjective sentences retained after the first phase. The effectiveness of the proposed methods is established through experimentation over customer reviews on different electronic products.
Multihop Backhaul Compression for the Uplink of Cloud Radio Access Networks
Venue: arXiv cs.IT 2013
Year: 2013

In cloud radio access networks (C-RANs), the baseband processing of the radio units (RUs) is migrated to remote control units (CUs). This is made possible by a network of backhaul links that connects RUs and CUs and that carries compressed baseband signals. While prior work has focused mostly on single-hop backhaul networks, this paper investigates efficient backhaul compression strategies for the uplink of C-RANs with a general multihop backhaul topology. A baseline multiplex-and-forward (MF) scheme is first studied in which each RU forwards the bit streams received from the connected RUs without any processing. It is observed that this strategy may cause significant performance degradation in the presence of a dense deployment of RUs with a well connected backhaul network. To obviate this problem, a scheme is proposed in which each RU decompresses the received bit streams and performs linear in-network processing of the decompressed signals. For both the MF and the decompress-process-and-recompress (DPR) backhaul schemes, the optimal design is addressed with the aim of maximizing the sum-rate under the backhaul capacity constraints. Recognizing the significant demands of the optimal solution of the DPR scheme in terms of channel state information (CSI) at the RUs, decentralized optimization algorithms are proposed under the assumption of limited CSI at the RUs. Numerical results are provided to compare the performance of the MF and DPR schemes, highlighting the potential advantage of in-network processing and the impact of CSI limitations.